Hyperspectral Image Classification Via Tensor Ridge Regression

In this paper, we investigate the ridge regression for multivariate labels by modelling each pixel and its surrounding pixels as a 3D tensor, and thereby propose a tensor ridge regression approach (TRR) for spatial-spectral hyperspectral image classification. Compared with the traditional ridge regression model, not only the spatial information is incorporated, but also the intrinsic spatial-spectral structure is captured. Moreover, the proposed TRR method is universal that it can be adopted to deal with the fusion of multiscale features for classification purpose. Experiment results conducted on two hyperspectral scenes demonstrate the effectiveness of the proposed method.